Class XGBoostOptions

java.lang.Object

org.tribuo.regression.xgboost.XGBoostOptions

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Options

public class XGBoostOptions
extends Object
implements com.oracle.labs.mlrg.olcut.config.Options

CLI options for configuring an XGBoost regression trainer.

Field Summary

Fields

Modifier and Type	Field	Description
float	alpha
int	depth
int	ensembleSize
float	eta
float	gamma
float	lambda
float	minWeight
int	numThreads
boolean	quiet
XGBoostRegressionTrainer.RegressionType	rType
float	subsample
float	subsampleFeatures

Fields inherited from interface com.oracle.labs.mlrg.olcut.config.Options

header

Constructor Summary

Constructors

Constructor	Description
XGBoostOptions()

Method Summary

Modifier and Type	Method	Description
XGBoostRegressionTrainer	getTrainer()	Gets the configured XGBoostRegressionTrainer.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.oracle.labs.mlrg.olcut.config.Options

getOptionsDescription

Field Details

rType

@Option(longName="xgb-regression-metric",
        usage="Regression type to use. Defaults to LINEAR.")
public XGBoostRegressionTrainer.RegressionType rType

ensembleSize

@Option(longName="xgb-ensemble-size",
        usage="Number of trees in the ensemble.")
public int ensembleSize

alpha

@Option(longName="xgb-alpha",
        usage="L1 regularization term for weights (default 0).")
public float alpha

minWeight

@Option(longName="xgb-min-weight",
        usage="Minimum sum of instance weights needed in a leaf (default 1, range [0,inf]).")
public float minWeight

depth

@Option(longName="xgb-max-depth",
        usage="Max tree depth (default 6, range (0,inf]).")
public int depth

eta

@Option(longName="xgb-eta",
        usage="Step size shrinkage parameter (default 0.3, range [0,1]).")
public float eta

subsampleFeatures

@Option(longName="xgb-subsample-features",
        usage="Subsample features for each tree (default 1, range (0,1]).")
public float subsampleFeatures

gamma

@Option(longName="xgb-gamma",
        usage="Minimum loss reduction to make a split (default 0, range [0,inf]).")
public float gamma

lambda

@Option(longName="xgb-lambda",
        usage="L2 regularization term for weights (default 1).")
public float lambda

quiet

@Option(longName="xgb-quiet",
        usage="Make the XGBoost training procedure quiet.")
public boolean quiet

subsample

@Option(longName="xgb-subsample",
        usage="Subsample size for each tree (default 1, range (0,1]).")
public float subsample

numThreads

@Option(longName="xgb-num-threads",
        usage="Number of threads to use (default 4, range (1, num hw threads)).")
public int numThreads

Constructor Details

XGBoostOptions

public XGBoostOptions()

Method Details

getTrainer

public XGBoostRegressionTrainer getTrainer()

Gets the configured XGBoostRegressionTrainer.

Returns:

The configured trainer.